Hallucinations occur when an AI model provides a completely fabricated answer, believing it to be a true fact. The model is convinced that it has produced the correct answer (and is confident in doing so), yet the answer it provides is inherently nonsensical. We observed this behavior in ChatGPT, but it is a phenomenon that can actually occur with all AI models, where the model asserts a confident prediction that ultimately proves to be inaccurate. The most effective approach to addressing this issue is to comprehend our models and their decision-making processes, a concept that is covered by a field I am excited to discuss – explainable AI (XAI)!
Watch the video...
https://youtu.be/6QVMCKmRJjQ?embedable=true&transcript=true
Now that we have had an introduction to explainability in AI, we can dive deep into the topic of AI ethics and discuss why explainability is such a crucial aspect, provided by my friend Auxane Boch, an expert in the field, a TUM IEAI research associate, and a freelancer.
At its core, explainability means that we understand how and why an AI system makes its decisions. This is important for several reasons. Firstly, without understanding the reasoning behind a decision, we can't blindly trust the system's output. This is particularly important in fields like healthcare or finance, where a wrong decision can have significant consequences. Secondly, without explainability, we can't identify and correct any biases or errors in decisions that may exist in the system. This is vital to ensure fairness, inclusivity, and the robustness of a system!
However, there are some major challenges to achieving explainability in AI systems. When we talk about explainability, we can see it from two angles: technical transparency - usually targeted at engineers or technical experts, and understandability - for all types of users, from a tech company CEO to your baker.
As you can imagine, understandability is a big challenge in itself! You cannot explain things in the same way to a highly digitally literate person as you would to your grandparents. Thus, one of the biggest challenges is the variation in digital literacy. Not everyone has the technical background to understand the complex algorithms and processes behind AI. This means that even if an explanation is provided, it may not be easily comprehensible to everyone. This could lead to a lack of trust in the system or even skepticism towards AI as a whole. In turn, this skepticism will impact the adoption of technologies and their acceptability.
Another challenge is the diversity of cultures we have the chance to see in our world. Different cultures may have different expectations regarding explainability and ways to understand - just as we know counting can be different depending on the culture. For example, some cultures may value transparency and a clear understanding of decision-making processes through visualization and a lot of details, while others may prioritize accurate outcomes over explanations. This means that AI developers must be aware of cultural differences and adapt their systems according to their target population.
In conclusion, explainability is a crucial aspect of AI ethics that enables us to trust and understand AI systems. However, achieving explainability is not without its challenges, including digital literacy and cultural differences. As we continue to develop and implement AI systems, we must strive to achieve an acceptable level of explainability while being aware of these challenges and working to overcome them.
I hope you've enjoyed this ethics-related piece and the introduction to XAI! Auxane shares many such short pieces in my weekly newsletter if that is something you find interesting as well!